code: def forward(self, x, dropout=0.0, intermediate_output=False):
      # x = x.view(-1, 3, 32, 32)
      y = self.DiscBlock1(x)
      y = self.block1(y)
      y = F.dropout(y, training=True, p=dropout)
      y = self.block2(y)
      y = F.dropout(y, training=True, p=dropout)
      y = self.block3(y)
      y = F.dropout(y, training=True, p=dropout)
      y = F.relu(y)
      y = y.view(x.size(0), 128, -1)
      y = y.mean(dim=2)
      critic_value = (
          self.l1(y).unsqueeze_(1).unsqueeze_(2)
      )  # or *.view(x.size(0), 128, 1, 1, 1)
  
      if intermediate_output:
          return (
              critic_value,
              y,
          )  # y is the D_(.), intermediate layer given in paper.
  
      return critic_value
  
imports: from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Discriminator obj's underlying member method code with example usage: obj.forward(x, dropout, intermediate_output) and returns: critic_value, critic_value,  y